Monday, April 20, 2026

J Curve and Solow Productivity Paradox are at Work with AI

Investors are going to keep challenging firms to show evidence their heavy artificial intelligence investments really are boosting productivity.


That is going to continue being a tough challenge, as history suggests the real output gains will take some time to develop.


So AI "productivity," or the "lack of quantifiable gains," are currently the most significant contemporary case of the Solow productivity paradox


In 1987, Nobel laureate Robert Solow famously remarked, "You can see the computer age everywhere but in the productivity statistics."


Recent research suggests productivity might actually decline for a time as firms deploy AI. 


The reason is the J curve


“We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains,” say economists Kristina McElheran; Mu-Jeung Yang; Zachary Kroff and Erik Brynjolfsson. “Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding,

while harming productivity and profitability in the short run.”


In other words, it takes time for enterprises to retool their business processes for the new technologies. And the more profound the innovations, perhaps the longer it takes to integrate those tools. 


Also, much of the reported AI adoption is horizontal rather than vertical; personal rather than systematic. In other words, individuals might be using chatbots, but workflows have yet to be transformed. 


So “personal productivity” has not yet been matched by an applied transformation of key work processes. And personal productivity gains are hard to measure, in terms of impact on firm performance. 


Agentic AI should help, as they can affect complex business processes. 


source: Forbe


Many have noted that  U.S. labor productivity significantly slowed in the 1970s and 1980s, despite rapid information technology investment. 


Then starting in the mid 1990s a decade of faster growth returned arguably because business process re-engineering had taken place.


A similar productivity paradox surrounds AI. As explained by economists Erik Brynjolfsson, Daniel Rock, and Chad Syverson in a 2017 working paper, AI and the Modern Productivity Paradox,” the paradox is primarily due to the time lag between technology advances and their impact on the economy. 


While technologies may advance rapidly, humans and our institutions change slowly. 


Moreover, the more transformative the technologies, the longer it takes for them to be embraced by companies and industries across the economy.


Translating technological advances into productivity gains requires major transformations, and therefore time.


Today, we see a "Modern AI Paradox": while Large Language Models (LLMs) and Generative AI are ubiquitous in headlines and corporate pilots, global aggregate productivity growth  remains sluggish.


Economists like Erik Brynjolfsson argue that the paradox isn't a failure of the technology, but a timing and structural issue. He identifies four main reasons for this lag:

  1. Mismeasurement: AI often improves quality, variety, or speed in ways that traditional GDP (which tracks "units produced") fails to capture.

  2. Redistribution: AI may be used for "rent-seeking" (competing for market share) rather than increasing total industry output.

  3. Implementation Lags: Significant "General Purpose Technologies" (like electricity or the steam engine) require decades of organizational restructuring before they move the needle.

  4. Mismanagement: Companies often use AI to automate old processes rather than inventing new, more efficient business models.


Study

Target Group

Productivity Impact Found

Notes on Enterprise Deployment Gaps

MIT/Stanford (NBER)

Customer Support Agents

14% increase in issues resolved per hour.

High-skilled workers saw less gain; impact was greatest on novices. Enterprises often fail to use AI as a "leveler" for training.

Harvard/BCG (SSRN)

Management Consultants

40% higher quality; 25% faster task completion.

"Jagged Frontier": AI failed spectacularly on certain logic tasks where humans over-relied on it, leading to "falling off the cliff" errors.

Microsoft/GitHub

Software Developers

55% faster at completing coding tasks.

Gains are often eaten by "code bloat" and increased technical debt if not managed by senior architects.

Goldman Sachs Research

Aggregate US Economy

Projected 1.5% annual increase over 10 years.

Real-world adoption is currently hindered by power grid constraints and data center infrastructure delays.

NBER / Brynjolfsson et al.

Generative AI & the "J-Curve"

Initial 0% or negative impact.

The "Productivity J-Curve": Measured productivity dips initially as firms invest in "intangible capital" (retraining, restructuring) before the payoff.


While individual tasks show gains, enterprise-wide productivity often remains flat for several reasons:

  • The "Pilot Trap": According to recent Adobe/Business research, 86 percent of IT leaders see potential, but only a fraction have moved beyond "isolated experiments" to organization-wide workflows

  • Inertial Workflows: Companies often use AI to "do the old thing faster" (e.g., writing more emails) rather than "doing the right thing" (e.g., eliminating the need for those emails entirely). This results in "Digital Overload"

  • The Human Bottleneck: AI can generate a report in seconds, but a human still takes hours to verify, edit, and approve it. Without changing the governance and approval structures, the AI speed gain is neutralized

  • Data Fragmentation: Most AI models are effective only if they can access clean, centralized data. Most enterprises still have "siloed" data, leading to AI hallucinations or irrelevant outputs

  • Skills Gap: Enterprises frequently treat AI as a "plug-and-play" tool like a calculator, failing to realize it requires a new type of "AI Literacy" to prompt and integrate effectively into complex projects.


None of that will be too comforting for suppliers who must justify their heavy AI capital investment. 


But history suggests the payoff is coming. It just will take some time. It always does.


Debating Amazon Leo Objectives

Amazon’s objectives with Leo are debated. 


Is this a standalone telecom business or a strategic infrastructure layer feeding higher-margin businesses (likely AWS)?


The possible motives are complicated as Amazon often talks like a “margin hunter,”  but often acts like a scale builder that tolerates thin margins for a time. 


The trick is that Amazon usually tries to separate where value is created from where it is captured. 

Amazon repeatedly enters markets characterized by low margin and high margin, so “margin” is not the primary consideration.


The effort to find “moats” or bottlenecks where value is extracted, and sometimes a low-margin business can lead to a high-margin moat. 


Layer

Characteristic

Amazon Behavior

Customer-facing layer

Huge TAM, fragmented, price-sensitive

Compete aggressively, often low margin

Infrastructure / platform layer

High fixed cost, scalable, defensible

Invest heavily, aim for high margin long term

Data / control layer

Feedback loops, optimization

Build moats that others can’t replicate


The point is that Amazon doesn’t mind entering a low-margin market if it helps it own a high-margin layer underneath or adjacent to it.


Also, “high capital investment” can be a feature, not a bug:

  • High CapEx deters competitors

  • Once built, marginal costs drop sharply

  • Scale converts fixed costs into a profit flywheel

  • Infrastructure can support multiple businesses

  • Pricing power eventually comes, once dominance is achieved.


So huge capex commitments are consistent with Amazon’s playbook, if Amazon believes it can control a bottleneck layer.


“Is this a high-margin or low-margin business?” might not be the right question for Amazon leaders, who likely are asking:

  • Can we own a critical layer?

  • Does this scale globally?

  • Does it reinforce our existing flywheels?

  • Can we improve cost structure vs incumbents?

  • Is there a hidden high-margin component?


So the larger picture is often not the immediate or obvious business, but the ability to create leverage elsewhere. Consumer initiatives such as e-commerce; devices or streaming then can be viewed as demand aggregators and ecosystem lock-in creators that drive revenue indirectly (advertising, cross selling, subscriber lock in).


Enterprise infrastructure plays such as AWS or logistics might be better examples of direct, high margin initiatives.


The thing about Leo is where it fits. From one point of view, consumer telecom is a low-margin, highly-competitive business with high regulatory conditions, low innovation and low growth rates. 


So why even consider it?


Amazon probably envisions non-obvious leverage points:

  • Where Amazon captures high-margin compute, not connectivity

  • With different value drivers in consumer and business markets.


Owning a connectivity service could:

  • Reduce internal costs

  • Improve performance (latency, reliability)

  • Be bundled with Prime and devices to

  • Drive usage of AWS, the advertising platform and e-commerce

  • Support IoT connectivity (devices, logistics, smart home). 


Framed that way, Leo might be viewed as a platform layer supporting:

  • Edge cloud

  • AWS (compute plus connectivity)

  • Telcos as customers

  • Prime average revenue per user or account

  • Customer retention and acquisition


To be sure, execution will matter. But, in theory, Leo is not directly about high margin. It is about control of what is likely to be a low-margin feature of a higher-margin ecosystem. 


Amazon’s explicit framing is straightforward:

  • Create a global broadband access business

  • Serving “tens of millions of customers” globally

  • in “unserved and underserved” markets

  • Offers private connectivity directly into AWS

  • for enterprise, government, and telecom customers.


So AWS integration, enterprise and government use cases and private networks might be key, not “consumer telecom.”


Leo then is a connectivity extension of AWS. 


But there are clear risks and some skeptics. 


Optimistically, Leo extends AWS to the edge of the network. 


On the other hand, it is a near-term drag on earnings, in a business with tough economics and financial returns that could take some time to develop.


So it might matter hugely whether Leo can generate AWS pull-through; enterprise demand and other ecosystem upsides. 


Also, how long this takes could matter. 


Layer

Role

Margin Potential

Consumer broadband (Leo ISP)

Distribution / scale

Low

Enterprise connectivity

Premium services

Medium

AWS integration layer

Data + compute + control

High

Ecosystem effects (Prime, commerce, ads)

Indirect monetization

Very high


Sure, it’s risky. But some will point to past Amazon initiatives based on entry into low-margin businesses that provided moats:

  • Retail → low margin → enabled AWS scale

  • Devices → low margin → enabled ecosystem lock-in

  • Logistics → low margin → enabled marketplace dominance.


Leo arguably fits the pattern, optimists will argue. It’s about high-margin AWS, not low-margin telecom. Skeptics will worry about the execution risk. 


J Curve and Solow Productivity Paradox are at Work with AI

Investors are going to keep challenging firms to show evidence their heavy artificial intelligence investments really are boosting productiv...